All public logs

Combined display of all available logs of AI Wiki. You can narrow down the view by selecting a log type, the username (case-sensitive), or the affected page (also case-sensitive).

01:08, 21 March 2023 Walle talk contribs created page Saver (Created page with "{{see also|Machine learning terms}} ==Saver in Machine Learning== In the context of machine learning, a '''Saver''' is a utility or class that enables users to save and restore the states of models, variables, or other components during the training and evaluation process. Saving the state of a model is important for various reasons, such as preserving intermediate results, facilitating transfer learning, and enabling the resumption of training after interruptions. Diffe...")
01:08, 21 March 2023 Walle talk contribs created page SavedModel (Created page with "{{see also|Machine learning terms}} ==SavedModel in Machine Learning== SavedModel is a standardized, language-agnostic, and platform-independent serialization format for machine learning models developed by Google as part of the TensorFlow framework. It facilitates the sharing, deployment, and management of trained models across different platforms, programming languages, and applications. ===Overview=== The primary objective of SavedModel is to streamline t...")
01:08, 21 March 2023 Walle talk contribs created page Parameter Server (PS) (Created page with "{{see also|Machine learning terms}} ==Parameter Server (PS) in Machine Learning== The '''Parameter Server (PS)''' is a distributed machine learning framework designed to manage the parameters of large-scale machine learning models during the training process. It is particularly useful when dealing with massive datasets and complex model architectures, which are common in Deep Learning and Distributed Machine Learning. ===Background=== Traditional machine learnin...")
01:07, 21 March 2023 Walle talk contribs created page PR AUC (area under the PR curve) (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the evaluation of classification models is a critical task. One common metric used to measure the performance of such models is the PR AUC, or Area Under the Precision-Recall (PR) Curve. The PR AUC is particularly useful when dealing with imbalanced datasets, where the proportion of positive and negative samples is unequal. ==Precision-Recall Curve== ===Definition=== The Precision-...")
11:45, 20 March 2023 Walle talk contribs created page One-shot learning (Created page with "{{see also|Machine learning terms}} ==One-shot Learning in Machine Learning== One-shot learning is a type of machine learning approach that aims to build robust models capable of learning from a limited amount of data, typically with only one or very few examples per class. This is in contrast to traditional supervised learning techniques, which require large amounts of labeled data for training. ===Background=== Traditional machine learning and deep learning algorithms...")
11:45, 20 March 2023 Walle talk contribs created page Objective function (Created page with "{{see also|Machine learning terms}} ==Objective Function in Machine Learning== The objective function, also known as the loss function or cost function, is a key concept in machine learning and optimization problems. It is a mathematical function that quantifies the discrepancy between the predicted output and the true output (ground truth) for a given input. The goal of machine learning algorithms is to minimize the value of the objective function to improve the...")
11:45, 20 March 2023 Walle talk contribs created page Objective (Created page with "{{see also|Machine learning terms}} ==Objective in Machine Learning== The objective in machine learning refers to the goal or aim that an algorithm strives to achieve through the learning process. This typically involves minimizing a loss function or maximizing a utility function, which are mathematical representations of the algorithm's performance. The objective provides guidance for the machine learning model to optimize its parameters and improve its predictions over...")
11:44, 20 March 2023 Walle talk contribs created page Novelty detection (Created page with "{{see also|Machine learning terms}} ==Novelty Detection in Machine Learning== Novelty detection is a sub-field of machine learning that focuses on the identification and classification of previously unseen, novel patterns or data points in a given dataset. The primary goal of novelty detection algorithms is to differentiate between normal and abnormal patterns, enabling effective decision-making in various applications, such as anomaly detection, outlier detectio...")
11:44, 20 March 2023 Walle talk contribs created page Non-response bias (Created page with "{{see also|Machine learning terms}} ==Non-response Bias in Machine Learning== Non-response bias, a type of sampling bias, occurs in machine learning when the data used for training and evaluating a model fails to accurately represent the entire population due to the absence or underrepresentation of certain subgroups in the sample. This phenomenon can lead to poor generalization performance, as the model's predictions may be systematically biased and not applicable t...")
11:44, 20 March 2023 Walle talk contribs created page Noise (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, noise refers to the presence of unwanted or irrelevant data that can have a detrimental effect on the performance and accuracy of a model. Noise can be introduced during the data collection process, data preprocessing, or through inherent randomness in the data itself. This article will provide an overview of the various types of noise, their sources, and their impacts on machine l...")
11:44, 20 March 2023 Walle talk contribs created page Node (TensorFlow graph) (Created page with "{{see also|Machine learning terms}} ==Node (TensorFlow graph)== In the context of machine learning, a node is a fundamental unit within a computational graph, which is a directed, acyclic graph (DAG) used to represent the flow of data and operations in a TensorFlow model. A TensorFlow graph is composed of multiple nodes, each representing an operation or a variable, which are connected by edges representing the flow of data between these nodes. The TensorFlow graph is a...")
11:44, 20 March 2023 Walle talk contribs created page Multinomial regression (Created page with "{{see also|Machine learning terms}} ==Multinomial Regression== Multinomial regression, also known as multinomial logistic regression or softmax regression, is a statistical method used in machine learning and statistics for modeling the relationship between a categorical dependent variable and one or more independent variables. It is an extension of binary logistic regression, which is used for predicting binary outcomes. Multinomial regression is particularly us...")
11:44, 20 March 2023 Walle talk contribs created page Multinomial classification (Created page with "{{see also|Machine learning terms}} ==Multinomial Classification== Multinomial classification, also known as multi-class or multi-nominal classification, is a type of supervised machine learning problem where the objective is to categorize an input data point into one of several discrete classes. In contrast to binary classification, where there are only two possible categories, multinomial classification deals with three or more categories. ===Problem Formulation==...")
11:43, 20 March 2023 Walle talk contribs created page Multi-class logistic regression (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Multi-class logistic regression''', also referred to as '''softmax regression''' or '''multinomial logistic regression''', is a supervised machine learning algorithm used for predicting the categorical label of an input instance when there are more than two possible classes. It is an extension of the binary logistic regression model, which can only handle two-class classification problems. Multi-class logistic r...")
11:43, 20 March 2023 Walle talk contribs created page Model training (Created page with "{{see also|Machine learning terms}} ==Introduction== Model training in machine learning refers to the process of developing a mathematical model capable of making predictions or decisions based on input data. This is achieved by iteratively adjusting the model's parameters until it can accurately generalize from the training data to previously unseen data. The ultimate goal of this process is to create a model that can perform well on new, real-world data without bei...")
11:43, 20 March 2023 Walle talk contribs created page Model capacity (Created page with "{{see also|Machine learning terms}} ==Definition== In the context of machine learning, ''model capacity'' refers to the ability of a model to learn and represent various functions and patterns within a given dataset. High-capacity models have a larger number of parameters and can therefore represent more complex functions, while low-capacity models have fewer parameters and are limited in the complexity of functions they can represent. Model capacity plays a crucial role...")
11:43, 20 March 2023 Walle talk contribs created page Minimax loss (Created page with "{{see also|Machine learning terms}} ==Minimax Loss== The minimax loss, also known as the minimax regret, is a performance measure in machine learning and game theory that quantifies the worst-case performance of an algorithm or decision rule under uncertainty. This concept is utilized in various optimization problems, where the goal is to minimize the maximum possible loss or regret under uncertain conditions. ===Definition=== Given a decision-making problem, th...")
11:43, 20 March 2023 Walle talk contribs created page Mini-batch stochastic gradient descent (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''mini-batch stochastic gradient descent''' ('''MB-SGD''') is an optimization algorithm commonly used for training neural networks and other models. The algorithm operates by iteratively updating model parameters to minimize a loss function, which measures the discrepancy between the model's predictions and actual target values. Mini-batch stochastic gradient descent is a variant of stochastic g...")
11:43, 20 March 2023 Walle talk contribs created page Metric (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, a '''metric''' refers to a quantitative measure that is used to evaluate the performance of an algorithm or model. Metrics help researchers and practitioners understand the effectiveness of their models in solving a particular task and allow for comparison with other models. Several types of metrics exist, each tailored to different types of tasks or problems, such as classification, regression...")
11:42, 20 March 2023 Walle talk contribs created page Matrix factorization (Created page with "{{see also|Machine learning terms}} ==Introduction== Matrix factorization is a technique in machine learning that aims to discover latent features underlying the interactions between two different kinds of entities. It has been widely used for tasks such as recommendation systems, dimensionality reduction, and data imputation. The primary goal of matrix factorization is to approximate a given matrix by factorizing it into two or more lower-dimensional matrices, which can...")
11:42, 20 March 2023 Walle talk contribs created page Matplotlib (Created page with "{{see also|Machine learning terms}} ==Introduction== '''Matplotlib''' is a widely used data visualization library in Python that enables developers to create high-quality and interactive visualizations, such as line plots, scatter plots, bar plots, histograms, 3D plots, and more. It is an essential tool in machine learning and data science for exploring and analyzing data, as well as presenting the results of models and algorithm...")
11:42, 20 March 2023 Walle talk contribs created page Loss surface (Created page with "{{see also|Machine learning terms}} ==Loss Surface in Machine Learning== In the field of machine learning, the '''loss surface''' (also referred to as the '''error surface''' or the '''objective function surface''') refers to the graphical representation of the relationship between the parameters of a learning model and the associated loss or error. The primary goal of machine learning algorithms is to optimize these parameters, minimizing the loss and consequently e...")
11:42, 20 March 2023 Walle talk contribs created page NumPy (Created page with "{{see also|Machine learning terms}} ==Introduction== NumPy (Numerical Python) is a highly popular and widely used open-source library in the field of machine learning and data science. NumPy provides a variety of tools and functions for working with numerical data in the Python programming language. It is highly regarded for its efficiency, simplicity, and performance in handling multi-dimensional arrays and matrices, as well as for its comprehensive suite of...")
11:42, 20 March 2023 Walle talk contribs created page NaN trap (Created page with "{{see also|Machine learning terms}} ==NaN Trap in Machine Learning== NaN trap, short for 'Not a Number' trap, is a common issue encountered in machine learning algorithms, particularly during the training process. It occurs when the algorithm's calculations yield undefined or unrepresentable numerical results, leading to the propagation of NaN values throughout the model. This can significantly hinder the model's learning capability and adversely affect its performance....")
11:42, 20 March 2023 Walle talk contribs created page Momentum (Created page with "{{see also|Machine learning terms}} ==Momentum in Machine Learning== Momentum is a widely-used optimization technique in the field of machine learning and deep learning, specifically in training neural networks. This method aims to accelerate the convergence of gradient-based optimization algorithms such as gradient descent and stochastic gradient descent by incorporating information from previous iterations. ===Gradient Descent and Stochastic Gradient Descent==...")
11:41, 20 March 2023 Walle talk contribs created page Metrics API (tf.metrics) (Created page with "{{see also|Machine learning terms}} ==Overview== The '''Metrics API''' in machine learning, specifically in the context of ''TensorFlow'' (TensorFlow), is a collection of utilities and classes designed to compute and represent various evaluation metrics, also known as performance metrics. These metrics are essential for evaluating the performance of machine learning models, and the Metrics API, referred to as '''tf.metrics''' in TensorFlow, facilitates the calculatio...")
11:41, 20 March 2023 Walle talk contribs created page Mean Squared Error (MSE) (Created page with "{{see also|Machine learning terms}} ==Mean Squared Error (MSE)== Mean Squared Error (MSE) is a widely used metric to evaluate the performance of regression models in machine learning. It measures the average of the squared differences between the predicted values and the actual values. MSE is suitable for evaluating continuous variables and is particularly useful when dealing with datasets that include outliers, as it emphasizes larger errors due to the squaring operatio...")
11:41, 20 March 2023 Walle talk contribs created page Mean Absolute Error (MAE) (Created page with "{{see also|Machine learning terms}} ==Mean Absolute Error (MAE)== The '''Mean Absolute Error (MAE)''' is a widely used metric in Machine Learning and Statistics to evaluate the performance of a predictive model. It measures the average magnitude of errors between the predicted and actual values, without considering the direction of the errors. MAE is a popular choice for regression tasks as it provides an easily interpretable representation of the model's error....")
05:06, 20 March 2023 Walle talk contribs created page Logits (Created page with "{{see also|Machine learning terms}} ==Logits in Machine Learning== In the field of machine learning, logits refer to the unnormalized probability values that are output by a classification model before they are transformed into actual probabilities. Logits are often associated with neural networks, particularly in the context of deep learning and artificial intelligence. These values serve as a crucial intermediate step in the process of predicting class prob...")
05:06, 20 March 2023 Walle talk contribs created page Least squares regression (Created page with "{{see also|Machine learning terms}} ==Introduction== In machine learning, '''Least Squares Regression''' is a well-established method for fitting a linear model to a set of data points. It seeks to minimize the sum of the squared differences between the observed values and the values predicted by the linear model. This technique is particularly useful in applications where the relationship between input features and the target variable is linear or near-linear. In this a...")
05:05, 20 March 2023 Walle talk contribs created page Items (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the term "items" typically refers to individual data points or instances that are used as inputs to train, validate, or test machine learning models. Items can take various forms, such as images, texts, or numerical values, depending on the specific problem being addressed. This article will explore the concept of items in machine learning, their significance in model training, and the...")
05:05, 20 March 2023 Walle talk contribs created page Item matrix (Created page with "{{see also|Machine learning terms}} ==Item Matrix in Machine Learning== The Item Matrix is a fundamental concept in machine learning, particularly in the realm of collaborative filtering and recommendation systems. It is a structured representation of items and their features, allowing algorithms to analyze patterns and provide personalized recommendations to users. This article delves into the definition, purpose, and applications of an Item Matrix, and offers a simplif...")
05:05, 20 March 2023 Walle talk contribs created page Inter-rater agreement (Created page with "{{see also|Machine learning terms}} ==Introduction== Inter-rater agreement, also referred to as inter-rater reliability or inter-annotator agreement, is a measure of the degree of consistency or consensus among multiple raters or annotators when evaluating a set of items, such as classifying data points in a machine learning task. This measure is essential in various machine learning and natural language processing (NLP) applications, where human-annotated data i...")
05:05, 20 March 2023 Walle talk contribs created page Instance (Created page with "{{see also|Machine learning terms}} ==Definition of Instance in Machine Learning== An '''instance''' in machine learning refers to a single data point or example used in the process of training and evaluating machine learning models. Instances are essential components of the dataset and are typically represented as a set of features and their corresponding labels or target values. They serve as the basis for learning patterns, making predictions, and evaluating the p...")
05:05, 20 March 2023 Walle talk contribs created page Individual fairness (Created page with "{{see also|Machine learning terms}} ==Individual Fairness in Machine Learning== Individual fairness in machine learning refers to the concept of ensuring that similar individuals are treated similarly by a machine learning algorithm. This idea has gained significant attention in recent years due to concerns about the potential for algorithmic bias and unfair treatment of individuals in various domains, including finance, healthcare, criminal justice, and hiring practices...")
05:05, 20 March 2023 Walle talk contribs created page Independently and identically distributed (i.i.d) (Created page with "{{see also|Machine learning terms}} ==Introduction== In the field of machine learning, the concept of ''independently and identically distributed'' (i.i.d) refers to a fundamental assumption about the nature of data used in statistical modeling and analysis. The i.i.d assumption is particularly important in the development of machine learning algorithms and their evaluation, as it affects the validity of the models and the accuracy of their predictions. The i.i.d assumpt...")
05:05, 20 March 2023 Walle talk contribs created page Incompatibility of fairness metrics (Created page with "{{see also|Machine learning terms}} ==Incompatibility of Fairness Metrics in Machine Learning== In the field of machine learning, fairness is a critical issue that has gained increasing attention in recent years. The concept of fairness is essential to ensure that algorithmic decisions are equitable and do not discriminate against particular groups. This article focuses on the incompatibility of fairness metrics in machine learning, its implications, and a simple explana...")
05:04, 20 March 2023 Walle talk contribs created page In-group bias (Created page with "{{see also|Machine learning terms}} ==In-group Bias in Machine Learning== In-group bias, also referred to as in-group favoritism or in-group preference, is a well-established phenomenon in social psychology. It occurs when individuals show a preference for members of their own group over those of other groups. In the context of machine learning, in-group bias refers to the unintentional favoring of specific groups in the algorithmic decision-making process, often resulti...")
05:04, 20 March 2023 Walle talk contribs created page Implicit bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Implicit bias in machine learning refers to the unintentional introduction of discriminatory or prejudiced behaviors, patterns, or outcomes in machine learning models, primarily due to the influence of biased training data or algorithmic design. These biases may manifest in the form of unfair treatment of certain demographic groups, perpetuation of stereotypes, or unequal allocation of resources. As machine learning sy...")
05:04, 20 March 2023 Walle talk contribs created page Hyperplane (Created page with "{{see also|Machine learning terms}} ==Definition== In machine learning, a '''hyperplane''' refers to a geometric construct that serves as a decision boundary for separating different classes or categories of data points in a multidimensional space. It is an essential concept for many classification and regression algorithms, including the popular Support Vector Machines (SVM) method. Mathematically, a hyperplane is an (n-1)-dimensional subspace within an n-dimens...")
05:04, 20 March 2023 Walle talk contribs created page Holdout data (Created page with "{{see also|Machine learning terms}} ==Holdout Data in Machine Learning== Holdout data is a subset of the dataset in machine learning that is separated from the training data and is used to evaluate the performance of a model during the model selection process. Holdout data helps to identify potential issues such as overfitting and provides an unbiased estimate of the model's generalization performance. This section discusses the importance of holdout data, the pr...")
05:04, 20 March 2023 Walle talk contribs created page Hinge loss (Created page with "{{see also|Machine learning terms}} ==Hinge Loss== Hinge loss is a type of loss function used in machine learning and specifically in support vector machines (SVMs). It measures the error between the predicted output and the actual output for a given training example. Hinge loss is particularly effective for binary classification problems, as it aims to find the optimal decision boundary (or margin) that maximally separates two classes of data points. ===Definit...")
05:04, 20 March 2023 Walle talk contribs created page Heuristic (Created page with "{{see also|Machine learning terms}} ==Definition of Heuristic== Heuristics, derived from the Greek word ''heuriskein'' which means "to discover," are problem-solving techniques that employ a practical approach to finding an adequate, though not always optimal, solution to complex problems. In machine learning, heuristics are often utilized to guide the search for an appropriate model or to optimize algorithmic parameters when an exhaustive search is computationally i...")
05:03, 20 March 2023 Walle talk contribs created page Hashing (Created page with "{{see also|Machine learning terms}} ==Hashing in Machine Learning== Hashing, a technique commonly used in computer science, has found various applications in the field of machine learning. In this context, hashing mainly refers to the process of converting high-dimensional input data into lower-dimensional representations, while preserving important information about the original data. This transformation can be beneficial for numerous machine learning tasks, including f...")
05:03, 20 March 2023 Walle talk contribs created page Hallucination (Created page with "{{see also|Machine learning terms}} ==Hallucination in Machine Learning== Hallucination in machine learning refers to the phenomenon where a model generates outputs that are not entirely accurate or relevant to the input data. This occurs when the model overfits to the training data or does not generalize well to new or unseen data. This behavior has been observed in various machine learning models, including deep learning models like neural networks and natural lang...")
05:03, 20 March 2023 Walle talk contribs created page Group attribution bias (Created page with "{{see also|Machine learning terms}} ==Introduction== Group attribution bias is a term used to describe a phenomenon in machine learning where an algorithm systematically and unfairly associates certain characteristics or outcomes with specific groups of individuals. This bias often stems from the training data that a machine learning model is exposed to, which may contain unrepresentative or skewed examples. When a model is trained on such data, it may inadvertently lear...")
05:03, 20 March 2023 Walle talk contribs created page Graph execution (Created page with "{{see also|Machine learning terms}} ==Graph Execution in Machine Learning== Graph execution in machine learning refers to a computational paradigm that employs directed graphs to represent and execute complex operations and dependencies between data, models, and algorithms. The graph execution approach is typically used in conjunction with TensorFlow, a popular open-source machine learning library, to optimize performance and parallelism in deep learning models. It p...")
05:03, 20 March 2023 Walle talk contribs created page Graph (Created page with "{{see also|Machine learning terms}} ==Introduction== In the context of machine learning, a '''graph''' is a mathematical structure that represents relationships between objects or entities, typically in the form of nodes (or vertices) connected by edges (or links). Graphs have become increasingly popular in recent years due to their ability to represent complex data and their effectiveness in solving various machine learning tasks. They are particularly useful for repres...")
05:03, 20 March 2023 Walle talk contribs created page Layers API (tf.layers) (Created page with "{{see also|Machine learning terms}} ==Introduction== The '''Layers API''' (commonly referred to as '''tf.layers''') is a high-level interface within the TensorFlow machine learning framework, specifically designed to simplify the process of building and training neural networks. It provides pre-built, reusable components, known as layers, that can be easily combined and customized to create a wide range of machine learning models. The Layers API encourages modular de...")
05:02, 20 March 2023 Walle talk contribs created page Kernel Support Vector Machines (KSVMs) (Created page with "{{see also|Machine learning terms}} ==Introduction== Kernel Support Vector Machines (KSVMs) are a class of machine learning algorithms that are particularly well-suited for classification and regression tasks. They are an extension of the Support Vector Machine (SVM) algorithm and utilize kernel functions to project data into a higher-dimensional space, allowing for nonlinear decision boundaries. This article aims to provide an academic-style overview of the key...")